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ing campaign by analyzing and understanding which neighborhood people are
more likely to hang out or shop in that speciic grocery store. Instead of blast-
ing a promotion to all neighborhoods, the communication can now be directed
to speciic neighborhoods, thereby increasing the eficiency of the market-
ing campaign. This analysis can possibly be conducted using 6-byte location
geohash over a span of one hour and inding all the cell phones that have visited
the grocery store regularly. A predictive model can compute the probability of
a customer visiting the grocery store based on their past hang out history, and
customer residence information can be clustered to identify neighborhoods most
likely to visit the shopping center.
Analysis of machine-to-machine transaction data using Big Data technolo-
gies is revolutionizing how location-based services can be personalized and
offered at low latency. Consider the example of Shopkick, a retail campaign tool
that can be downloaded on a smartphone. Shopkick seeks and uses location data
to offer campaigns. Once the app is downloaded, Shopkick seeks permission to
use current location as recorded by the smartphone. In addition, Shopkick has a
database of retailers and their geo-locations. It runs campaigns on behalf of the
merchants and collects its revenues from merchants. Shopkick will let me know,
for example, that the department store in my neighborhood would like me to
visit the store. As a further incentive, Shopkick will deposit shopping points in
my account for just visiting the store. As I walk through the store, Shopkick can
use my current location in the smartphone to record my presence at the store and
award points.
Jeff Jonas provided me tremendous motivation for playing with location data.
I used
openpaths.cc
, a site that tracks cell phone location, to track my where-
abouts for approximately three months. Watching my movements over these
months was like having a video unfold my activities event by event. I could also
see how I could improve the accuracy of the location data collected by openpaths
with other known information such as street maps. With the help of a business
directory, it is easy to ind out the number and duration of my trips to Starbucks,
Tokyo Joe's, and Sweet Tomato, my three most common eating hang outs.
Why would a customer “opt-in”? Device makers, CSPs, and retailers are
beginning to offer a number of location-based services, in exchange for location
“opt-in.” For example, smartphones offer “ind my phone” services, which
can locate a phone. If the phone is lost, the last known location can be ascer-
tained via a website. In exchange, the CSP or the device manufacturer may
seek location data for product or service improvement. These location-based
services could also be revenue generating. A CSP may decide to charge for a
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